class_docstr: Normalizes the observations to have zero mean and standard deviation of one,
  i.e. it applies whitening to the inputs.
  
  This typically helps significantly with learning, especially if different dimensions are
  on different scales. However, it complicates training in the sense that you will have to store
  these weights alongside the policy if you intend to load it later. It is the responsibility of
  the user to do so.
code: def __init__(self):
      self.normalizer = None
  
file_path: keras-rl/rl/processors.py
imports: import numpy
  from rl.core import Processor
  from rl.util import WhiteningNormalizer
label_desc: Use image node_id 1 to create a new WhiteningNormalizerProcessor object from inherited base classes: Processor with example: obj = WhiteningNormalizerProcessor()